Collaborative Research: SDCI HPC Improvement: Improvement and Support of Community Based Dense Linear Algebra Software for Extreme Scale Computational Science

合作研究：SDCI HPC 改进：针对超大规模计算科学的基于社区的密集线性代数软件的改进和支持

• 批准号: 1032815
• 负责人: Jack Dongarra
• 金额: $ 80万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1032815&HistoricalAwards=false
• 关键词: Collaborative; Research; SDCI; HPC; Improvement

Numerous recent federal and academic reports comment on the inadequacy of software support for High Performance Computing. Among other deficiencies, much of the software designed for HPC, such as numerical libraries that encapsulate complicated and widely used algorithms, is considered too hard to use, too inefficient (too low a fraction of peak and/or not scalable), or both. However, the linear algebra libraries LAPACK and ScaLAPACK are frequently mentioned as positive examples whose success in this area should be emulated.This work involves the maintenance, enhancement, hardening, and modernizing of the LAPACK and ScaLAPACK libraries in the context of the ongoing revolution in processor architecture and system design. These libraries are widely used: there were over 160 million web hits (excluding bots) from 2004-2009, and they have been adopted by many vendors as the basis of their own libraries: Intel, Cray, IBM, HP, Fujitsu, NEC, NAG, IMSL, and the Mathworks (producers of Matlab). There is every reason to believe, therefore, that improving these stalwart components of the HPC software stack will have a very large impact.

许多最近的联邦和学术报告评论了对高性能计算的软件支持的不足。在其他缺陷中，许多为高性能计算设计的软件，如封装复杂和广泛使用的算法的数字库，被认为太难使用，效率太低（峰值的一小部分太低和/或不可扩展），或者两者兼而有之。然而，线性代数库LAPACK和ScaLAPACK经常被作为积极的例子提到，它们在这一领域的成功应该被模仿。这项工作涉及在处理器架构和系统设计不断变革的背景下对LAPACK和ScaLAPACK库进行维护、增强、加固和现代化。这些库被广泛使用：从2004年到2009年，有超过1.6亿的网络点击量（不包括机器人），并且它们已经被许多供应商采用为他们自己的库的基础：英特尔、Cray、IBM、HP、富士通、NEC、NAG、IMSL和Mathworks （Matlab的生产者）。因此，我们有充分的理由相信，改进这些高性能计算软件堆栈的中坚组件将产生非常大的影响。